{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is a Variable?\n",
    "\n",
    "A variable is any characteristics, number, or quantity that can be measured or counted. For example:\n",
    "\n",
    "- Age (21, 35, 62, ...)\n",
    "- Gender (male, female)\n",
    "- Income (GBP 20000, GBP 35000, GBP 45000, ...)\n",
    "- House price (GBP 350000, GBP 570000, ...)\n",
    "- Country of birth (China, Russia, Costa Rica, ...)\n",
    "- Eye colour (brown, green, blue, ...)\n",
    "- Vehicle make (Ford, VolksWagen, ...)\n",
    "\n",
    "...are examples of variables. They are called 'variable' because the value they take may vary (and it usually does) in a population. \n",
    "\n",
    "Most variables in a data set can be classified into one of two major types:\n",
    "\n",
    "**Numerical variables** and **categorical variables**\n",
    "\n",
    "In this notebook, I will discuss Categorical variables\n",
    "\n",
    "===================================================================================\n",
    "\n",
    "\n",
    "## Categorical variables\n",
    "\n",
    "\n",
    "The values of a categorical variable are selected from a group of **categories**, also called **labels**. Examples are gender (male or female) and marital status (never married, married, divorced or widowed).\n",
    "Other examples of categorical variables include:\n",
    "\n",
    "- Intended use of loan (debt-consolidation, car purchase, wedding expenses, ...)\n",
    "- Mobile network provider (Vodafone, Orange, ...)\n",
    "- Postcode\n",
    "\n",
    "Categorical variables can be further categorised into ordinal and nominal variables.\n",
    "\n",
    "### Ordinal categorical variables\n",
    "\n",
    "Categorical variable in which categories can be meaningfully ordered are called ordinal. For example:\n",
    "\n",
    "- Student's grade in an exam (A, B, C or Fail).\n",
    "- Days of the week can be ordinal with Monday = 1 and Sunday = 7.\n",
    "- Educational level, with the categories Elementary school,  High school, College graduate and PhD ranked from 1 to 4. \n",
    "\n",
    "### Nominal categorical variable\n",
    "\n",
    "There isn't an intrinsic order of the labels. For example, country of birth (Argentina, England, Germany) is nominal. Other examples of nominal variables include:\n",
    "\n",
    "- Postcode\n",
    "- Vehicle make (Citroen, Peugeot, ...)\n",
    "\n",
    "There is nothing that indicates an intrinsic order of the labels, and in principle, they are all equal.\n",
    "\n",
    "\n",
    "**To be considered:**\n",
    "\n",
    "Sometimes categorical variables are coded as numbers when the data are recorded (e.g. gender may be coded as 0 for males and 1 for females). The variable is still categorical, despite the use of numbers.\n",
    "\n",
    "In a similar way, individuals in a survey may be coded with a number that uniquely identifies them (for example to avoid storing personal information for confidentiality). This number is really a label, and the variable then categorical. The number has no meaning other than making it possible to uniquely identify the observation (in this case the interviewed subject).\n",
    "\n",
    "Ideally, when we work with a dataset in a business scenario, the data will come with a dictionary that indicates if the numbers in the variables are to be considered as categories or if they are numerical. And if the numbers are categoriies, the dictionary would explain what they intend to represent.\n",
    "\n",
    "=============================================================================\n",
    "\n",
    "## Real Life example: Peer to peer lending (Finance)\n",
    "\n",
    "### Lending Club\n",
    "\n",
    "**Lending Club** is a peer-to-peer Lending company based in the US. They match people looking to invest money with people looking to borrow money. When investors invest their money through Lending Club, this money is passed onto borrowers, and when borrowers pay their loans back, the capital plus the interest passes on back to the investors. It is a win for everybody as they can get typically lower loan rates and higher investor returns.\n",
    "\n",
    "If you want to learn more about Lending Club follow this [link](https://www.lendingclub.com/).\n",
    "\n",
    "The Lending Club dataset contains complete loan data for all loans issued through 2007-2015, including the current loan status (Current, Late, Fully Paid, etc.) and latest payment information. Features include credit scores, number of finance inquiries, address including zip codes and state, and collections among others. Collections indicates whether the customer has missed one or more payments and the team is trying to recover their money.\n",
    "\n",
    "The file is a matrix of about 890 thousand observations and 75 variables. More detail on this dataset can be found in [Kaggle's website](https://www.kaggle.com/wendykan/lending-club-loan-data)\n",
    "\n",
    "Let's go ahead and have a look at the variables!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "====================================================================================================\n",
    "\n",
    "To download the Lending Club loan book from Kaggle go to this [website](https://www.kaggle.com/wendykan/lending-club-loan-data)\n",
    "\n",
    "Scroll down to the bottom of the page, and click on the link 'loan.csv', and then click the 'download' blue button towards the right of the screen, to download the dataset.\n",
    "Unzip it, and save it to a directory of your choice.\n",
    "\n",
    "**Note that you need to be logged in to Kaggle in order to download the datasets**.\n",
    "\n",
    "If you save it in the same directory from which you are running this notebook, then you can load it the same way I will load it below.\n",
    "\n",
    "===================================================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131079</th>\n",
       "      <td>5554979</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384935</th>\n",
       "      <td>16552323</td>\n",
       "      <td>OWN</td>\n",
       "      <td>Current</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73585</th>\n",
       "      <td>8185454</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>credit_card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660814</th>\n",
       "      <td>56180166</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349757</th>\n",
       "      <td>16772363</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Current</td>\n",
       "      <td>credit_card</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id home_ownership loan_status             purpose\n",
       "131079   5554979           RENT     Current  debt_consolidation\n",
       "384935  16552323            OWN     Current                 car\n",
       "73585    8185454           RENT  Fully Paid         credit_card\n",
       "660814  56180166           RENT     Current  debt_consolidation\n",
       "349757  16772363           RENT     Current         credit_card"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's load the dataset with just a few columns and a few rows,\n",
    "# to speed things up\n",
    "\n",
    "use_cols = ['id', 'purpose', 'loan_status', 'home_ownership']\n",
    "\n",
    "data = pd.read_csv(\n",
    "    'loan.csv', usecols=use_cols).sample(\n",
    "        10000, random_state=44)  # set a seed for reproducibility\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['RENT', 'OWN', 'MORTGAGE', 'OTHER'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's inspect the variable home ownership,\n",
    "# which indicates whether the borrowers own their home\n",
    "# or if they are renting for example, among other things.\n",
    "\n",
    "data.home_ownership.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x37a4b3f630>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAE4CAYAAACjVX8qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x37a847d080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's make a bar plot, with the number of loans\n",
    "# for each category of home ownership\n",
    "\n",
    "fig = data['home_ownership'].value_counts().plot.bar()\n",
    "fig.set_title('Home Ownership')\n",
    "fig.set_ylabel('Number of customers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of the borrowers either own their house on a mortgage or rent their property. A few borrowers own their home completely. The category 'Other' seems to be empty. To be completely sure, we could print the numbers as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MORTGAGE    4957\n",
       "RENT        4055\n",
       "OWN          986\n",
       "OTHER          2\n",
       "Name: home_ownership, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['home_ownership'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 2 borrowers that have other arrangements for their property. For example, they could live with their parents, or live in a hotel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['debt_consolidation', 'car', 'credit_card', 'small_business',\n",
       "       'house', 'moving', 'other', 'home_improvement', 'medical',\n",
       "       'major_purchase', 'vacation', 'educational', 'wedding',\n",
       "       'renewable_energy'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the \"purpose\" variable is another categorical variable\n",
    "# that indicates how the borrowers intend to use the\n",
    "# money they are borrowing, for example to improve their\n",
    "# house, or to cancel previous debt.\n",
    "\n",
    "data.purpose.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x37a88f3470>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAFiCAYAAAAOQFS7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvofymAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x37a847deb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's make a bar plot with the number of borrowers\n",
    "# within each category\n",
    "\n",
    "fig = data['purpose'].value_counts().plot.bar()\n",
    "fig.set_title('Loan Purpose')\n",
    "fig.set_ylabel('Number of customers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of the borrowers intend to use the loan for 'debt consolidation' or to repay their 'credit cards'. This is quite a common among borrowers. What the borrowers intend to do is, to consolidate all the debt that they have on different financial items, in one single debt, the new loan that they will take from Lending Club in this case. This loan will usually provide an advantage to the borrower, either in the form of lower interest rates than a credit card, for example, or longer repayment period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Current', 'Fully Paid', 'Default', 'Charged Off',\n",
       "       'Late (31-120 days)', 'Issued', 'In Grace Period',\n",
       "       'Does not meet the credit policy. Status:Fully Paid',\n",
       "       'Does not meet the credit policy. Status:Charged Off',\n",
       "       'Late (16-30 days)'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's look at one additional categorical variable,\n",
    "# \"loan status\", which represents the current status\n",
    "# of the loan. This is whether the loan is still active\n",
    "# and being repaid, or if it was defaulted,\n",
    "# or if it was fully paid among other things.\n",
    "\n",
    "data.loan_status.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x37a89eb518>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAICCAYAAAA3XEF8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIUlaNpuHJGnZ/g3MxFUYca5xXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x37a894fc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's make a bar plot with the number of borrowers\n",
    "# within each category\n",
    "\n",
    "fig = data['loan_status'].value_counts().plot.bar()\n",
    "fig.set_title('Status of the Loan')\n",
    "fig.set_ylabel('Number of customers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the majority of the loans are active (current) and a big number have been 'Fully paid'. The remaining labels have the following meaning:\n",
    "- Late (16-30 days): customer missed a payment\n",
    "- Late (31-120 days): customer is behind in payments for more than a month\n",
    "- Charged off: the company declared that they will not be able to recover the money for that loan ( money is typically lost)\n",
    "- Issued: loan was granted but money not yet sent to borrower\n",
    "- In Grace Period: window of time agreed with customer to wait for payment, usually, when customer is behind in their payments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131079     5554979\n",
       "384935    16552323\n",
       "73585      8185454\n",
       "660814    56180166\n",
       "349757    16772363\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finally, let's look at a variable that is numerical,\n",
    "# but its numbers have no real meaning, and therefore\n",
    "# should be better considered as a categorical one.\n",
    "\n",
    "data['id'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, each id represents one customer. This number is assigned in order to identify the customer if needed, while \n",
    "maintaining confidentiality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The variable has as many different id values as customers,\n",
    "# in this case 10000, because we loaded randomly \n",
    "# 10000 rows/customers from the original dataset.\n",
    "\n",
    "len(data['id'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**That is all for this demonstration. I hope you enjoyed the notebook, and see you in the next one.**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": true
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
